# MIT License
#
# Copyright (C) IBM Corporation 2019
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
# persons to whom the Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import json
import argparse
import os

from robustness import robustness_evaluation


def get_secret(path, default=''):
    try:
        with open(path, 'r') as f:
            cred = f.readline().strip('\'')
        f.close()
        return cred
    except Exception:
        return default


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--epsilon', type=float,
                        help='Epsilon value for the FGSM attack', default=0.2)
    parser.add_argument('--model_id', type=str,
                        help='Training model id', default="training-dummy")
    parser.add_argument('--metric_path', type=str,
                        help='Path for robustness check output', default="/tmp/robustness.txt")
    parser.add_argument('--robust_status', type=str,
                        help='Path for robustness status output', default="/tmp/status.txt")
    parser.add_argument('--model_class_file', type=str,
                        help='pytorch model class file', default="model.py")
    parser.add_argument('--model_class_name', type=str,
                        help='pytorch model class name', default="model")
    parser.add_argument('--loss_fn', type=str,
                        help='PyTorch model loss function', default="torch.nn.CrossEntropyLoss()")
    parser.add_argument('--optimizer', type=str,
                        help='pytorch model optimizer', default="torch.optim.Adam(model.parameters(), lr=0.001)")
    parser.add_argument('--clip_values', type=str,
                        help='pytorch model clip_values allowed for features (min, max)', default="(0,1)")
    parser.add_argument('--nb_classes', type=int,
                        help='The number of classes of the model', default=2)
    parser.add_argument('--input_shape', type=str,
                        help='The shape of one input instance for the pytorch model', default="(1,3,64,64)")
    parser.add_argument('--feature_testset_path', type=str,
                        help='Feature test dataset path in the data bucket', default="processed_data/X_test.npy")
    parser.add_argument('--label_testset_path', type=str,
                        help='Label test dataset path in the data bucket', default="processed_data/y_test.npy")
    parser.add_argument('--data_bucket_name', type=str,
                        help='Bucket that has the processed data', default="training-data")
    parser.add_argument('--result_bucket_name', type=str,
                        help='Bucket that has the training results', default="training-result")
    parser.add_argument('--adversarial_accuracy_threshold', type=float,
                        help='Model accuracy threshold on adversarial samples', default=0.2)
    args = parser.parse_args()

    epsilon = args.epsilon
    metric_path = args.metric_path
    model_id = args.model_id
    robust_status = args.robust_status
    model_class_file = args.model_class_file
    model_class_name = args.model_class_name
    LossFn = args.loss_fn
    Optimizer = args.optimizer
    nb_classes = args.nb_classes
    feature_testset_path = args.feature_testset_path
    label_testset_path = args.label_testset_path
    data_bucket_name = args.data_bucket_name
    result_bucket_name = args.result_bucket_name
    clip_values = eval(args.clip_values)
    input_shape = eval(args.input_shape)
    adversarial_accuracy_threshold = args.adversarial_accuracy_threshold

    object_storage_url = get_secret('/app/secrets/s3_url', 'minio-service:9000')
    object_storage_username = get_secret('/app/secrets/s3_access_key_id', 'minio')
    object_storage_password = get_secret('/app/secrets/s3_secret_access_key', 'minio123')

    metrics = robustness_evaluation(object_storage_url, object_storage_username, object_storage_password,
                                    data_bucket_name, result_bucket_name, model_id,
                                    feature_testset_path=feature_testset_path,
                                    label_testset_path=label_testset_path,
                                    clip_values=clip_values,
                                    nb_classes=nb_classes,
                                    input_shape=input_shape,
                                    model_class_file=model_class_file,
                                    model_class_name=model_class_name,
                                    LossFn=LossFn,
                                    Optimizer=Optimizer,
                                    epsilon=epsilon)

    if not os.path.exists(os.path.dirname(metric_path)):
        os.makedirs(os.path.dirname(metric_path))
    with open(metric_path, "w") as report:
        report.write(json.dumps(metrics))

    robust = "true"
    if metrics['model accuracy on adversarial samples'] < adversarial_accuracy_threshold:
        robust = "false"

    if not os.path.exists(os.path.dirname(robust_status)):
        os.makedirs(os.path.dirname(robust_status))
    with open(robust_status, "w") as report:
        report.write(robust)
